CVE-2025-51859 is a stored Cross-Site Scripting (XSS) vulnerability affecting the Chaindesk platform, specifically its agent chat component. The vulnerability arises from the way Chaindesk integrates Large Language Models (LLMs) into its chat agents. An attacker can craft a malicious AI agent by manipulating the system prompt given to the underlying LLM, instructing it to embed malicious script payloads, such as SVG-based XSS vectors, into the chat responses. When an end user interacts with this malicious AI agent or accesses a direct link to a conversation containing the embedded XSS payload, the malicious script executes within the user's browser context. This execution can lead to the theft of sensitive information, notably JSON Web Tokens (JWTs) used for session authentication, which can result in account hijacking. The vulnerability is stored, meaning the malicious payload persists within the chat history or conversation logs, increasing the risk of repeated exploitation. No specific affected versions are listed, and no patch information is currently available. The vulnerability was published on July 22, 2025, and no known exploits in the wild have been reported yet. The lack of a CVSS score indicates that the vulnerability is newly disclosed and pending further assessment.

For European organizations using Chaindesk, this vulnerability poses a significant risk to confidentiality and integrity of user sessions and data. The ability to execute arbitrary scripts in users' browsers can lead to session token theft, enabling attackers to impersonate legitimate users and access sensitive corporate resources. This can result in data breaches, unauthorized access to internal systems, and potential lateral movement within the network. Given the stored nature of the XSS, multiple users may be exposed over time, increasing the attack surface. Organizations relying on Chaindesk for customer support or internal communications may face reputational damage and regulatory consequences under GDPR if personal data is compromised. The threat is particularly concerning for sectors with high security requirements, such as finance, healthcare, and government agencies across Europe. The absence of known exploits suggests a window of opportunity for proactive mitigation before widespread exploitation occurs.

European organizations should immediately audit their use of Chaindesk, especially the agent chat components that integrate LLMs. Specific mitigation steps include: 1) Restricting or sanitizing system prompts used to generate AI agent responses to prevent injection of malicious scripts. 2) Implementing robust output encoding and Content Security Policy (CSP) headers to mitigate the impact of any injected scripts. 3) Monitoring chat logs and conversation histories for suspicious or unexpected script tags or SVG elements. 4) Applying strict access controls and multi-factor authentication to reduce the impact of potential session hijacking. 5) Engaging with Chaindesk vendors or support channels to obtain patches or updates addressing this vulnerability as soon as they become available. 6) Educating users about the risks of interacting with untrusted AI agents or clicking on direct conversation links from unknown sources. 7) Employing web application firewalls (WAFs) with custom rules to detect and block malicious payloads targeting the chat interface. These measures go beyond generic advice by focusing on the unique integration of LLMs and the stored nature of the XSS in this context.